Abstract
A new approach to the optimal design of power inverters for on-grid photovoltaic systems that uses genetic algorithms (GA) is provided in this article. The nonlinear average model is adopted to model the conversion stage in order to accurately evaluate and quickly estimate the power losses of the power devices. The hysteresis current control that guarantees a quasi-sinusoidal output current is applied to generate the inverter control signals. The design of the solar inverter must meet three contradictory objectives that need to be optimized at the same time. In fact, the aim is to maximize the efficiency of the converter while minimizing its size and price under electrical constraints. The problem variables are the output current ripple and the passive and active components available on the market (IGBTs/MOSFETs, Diodes, Inductors). NSGA-II (Elitist Nondominated Sorting Genetic Algorithm) is appropriate in the case where discrete design variables are used to search for optimal Pareto solutions. It carries out a systematic and efficient search among the developed databases for a set of components which define the optimal structures of the inverter. The introduced method makes the design task easier since the best solutions depend on the components available on the market and significantly reduces the time to market for manufacturers.
Highlights
According to the BP Statistical Review of World Energy [1], global energy demand has increased by 2.9% in 2018, nearly twice the average annual growth in demand over the past decade (+1.5%/year) and the fastest since 2010
A strong growth in the integration of renewable energy sources into power plants worldwide has been recorded [2,3,4,5,6,7,8,9]. us, progressive advances of renewable energy power generation systems have led to the search for solutions to improve the performances of these systems, mainly by taking advantage of the enormous developments in the power semiconductor industry
Power electronics are at the core of power generation systems since they provide the technology between sources and loads that converts energy from its continuous form into alternative or inverse [10]
Summary
According to the BP Statistical Review of World Energy [1], global energy demand has increased by 2.9% in 2018, nearly twice the average annual growth in demand over the past decade (+1.5%/year) and the fastest since 2010. Hysteresis control is one of the simplest nonlinear mechanisms to ensure spontaneous current regulation in power systems [23, 24] It will directly define the state of the switches in the H-bridge topology to track the output current Iout to its reference Iref with a fixed value for the current ripple given by ΔI. E use of hysteresis control with model based on electrical circuit does not pose any problems since the switches are controlled by the generic gate signals (high for conduction state and low for blocking one). This is not the case for the average models where switches are monitored by duty cycle. An estimation of the maximum switching frequency fs,max as function of power system specifications should be done
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